Open Access

ARTICLE

Narwhal Optimizer: A Nature-Inspired Optimization Algorithm for Solving Complex Optimization Problems

Raja Masadeh¹, Omar Almomani^2,*, Abdullah Zaqebah¹, Shayma Masadeh³, Kholoud Alshqurat³, Ahmad Sharieh⁴, Nesreen Alsharman⁵

1 Computer Science Department, The World Islamic Sciences and Education University, Amman, 11947, Jordan
2 Department of Networks and Cybersecurity, Al-Ahliyya Amman University, Amman, 19111, Jordan
3 Academic Services Department, The World Islamic Sciences and Education University, Amman, 11947, Jordan
4 Computer Science Department, The University of Jordan, Amman, 11942, Jordan
5 Department of Computer Science, German Jordan University, Madaba, 11180, Jordan

* Corresponding Author: Omar Almomani. Email:

(This article belongs to the Special Issue: Advanced Bio-Inspired Optimization Algorithms and Applications)

Computers, Materials & Continua 2025, 85(2), 3709-3737. https://doi.org/10.32604/cmc.2025.066797

Received 07 May 2025; Accepted 20 June 2025; Issue published 23 September 2025

Abstract

This research presents a novel nature-inspired metaheuristic optimization algorithm, called the Narwhale Optimization Algorithm (NWOA). The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals, “unicorns of the sea”, particularly the use of their distinctive spiral tusks, which play significant roles in hunting, searching prey, navigation, echolocation, and complex social interaction. Particularly, the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice. These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation, convergence speed, and solution accuracy. The performance of the NWOA is evaluated on 30 benchmark test functions. A comparison study using the Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Perfumer Optimization Algorithm (POA), Candle Flame Optimization (CFO) Algorithm, Particle Swarm Optimization (PSO) Algorithm, and Genetic Algorithm (GA) validates the results. As evidenced in the experimental results, NWOA is capable of yielding competitive outcomes among these well-known optimizers, whereas in several instances. These results suggest that NWOA has proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.

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Keywords

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